Soft Skills That Every Data Scientist Should Possess

No technical person can thrive with technical skills alone. Data scientists are no exception. While mastering the tools and techniques needed for data science is a challenge in itself, combining these skills with a set of practised soft skills is what really takes a data scientist the extra mile. A survey conducted by Google revealed that a team consisting of interdisciplinary people with a balance between hard skills and soft skills performs the best. So, without any further ado let us look at five such soft skills which are essential for a data scientist.

Communication skills

There is hardly a field which does not need for its practitioners to have communication skills. Data science professionals have a special need for these skills because their work loses all meaning if it is not supported by effective communication. The data scientists need to convince the stakeholders about the effectiveness of a certain endeavour – how a certain piece of insight can support business. After the findings are ready they need to explain the significance of those findings to the rest of the team. Not only does this help the company to really benefit from the data scientist’s work but also builds up the trust and reliance between them.

Eye for problems

A data science professional’s task does not end at solving the problem that is apparent. A keen eye for gaps and inefficiencies in various business processes helps the data science professional add more value to the business. These skills come really handy when a data scientist tries to offer some sort of prescriptive analysis. It really helps them see all corners of a situation so as to modify the solution. This is basically an extension of the curiosity and imagination in a person.

Domain knowledge

It becomes very difficult for the data scientist to offer any real help unless he is really aware of the market the business is operating within. There is more to solving a problem than numbers and statistics; one needs to understand the labour situation, the logistic crises, the political milieu. A data scientist in Malaysia with a practical understanding of the atmosphere that surrounds the business is an asset.

Demonstrative skills

Some say it is an extension of communication skills and some call it storytelling. However, I strongly feel that demonstration needs to be considered and learnt as a separate skill.

Businesses are pouring out resources to gain data centric insights, they are hiring skilled data science professionals, buying expensive software to harness the power of big data. In spite of all of these things only a fraction of the businesses applying data science are reaping any significant returns of their investments. This is often a result of lack of data literacy among the teams and a gap between the data professionals and the rest of the company. A data scientist that can demonstrate his or her work as well as the role of others to create a data oriented culture, can really cause a paradigm shift for the better.

The data scientist’s objective stance as a technical professional is supported by numbers, but their subjective existence in the company as a problem solver and an influencer depends on these soft skills.

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